--- redirect_from: - "/03/test-accuracy/accuracy" interact_link: content/03/Test_Accuracy/Accuracy.ipynb kernel_name: python3 kernel_path: content/03/Test_Accuracy has_widgets: false title: |- Test Accuracy pagenum: 3 prev_page: url: /results.html next_page: url: /03/Experiments_Faces-HQ/Faces-HQ.html suffix: .ipynb search: accuracy comment: "***PROGRAMMATICALLY GENERATED, DO NOT EDIT. SEE ORIGINAL FILES IN /content***" ---
Test Accuracy

Accuracy 

#Test accuracy using SVM classifier.
import plotly.graph_objects as go
import plotly
from IPython.core.display import display, HTML
import pandas as pd
accuracyData = pd.DataFrame({'x': [0, 0, 0, 0, 0, 0, 0,100,100, 100,100,100,100, 200, 200, 200, 200, 200, 300, 300, 300, 300, 400,400,400, 500, 500,600],
                                    'labels': ['58%', '69%', '85%', '89%', '98%', '100%', '100%','72%','86%','89%','98%','100%','100%', '85%','87%','99%','100%','100%', '84%','98%','100%','100%', '93%','100%', '100%', '100%', '100%','100%'],
                                    'y': [100, 200, 300, 400, 500, 600, 722,200,300, 400,500,600,722, 300,400,500,600,722,400,500,600,722,500,600,722,600,722, 722],
                                   'percent':[58, 69, 85, 89, 98,100,100,72,86,89,98,100,100,85,87,99,100,100,84,98,100,100,93,100,100, 100,100,100]})
fig = go.Figure(data=go.Scatter(
    x=accuracyData["x"],
    y=accuracyData["y"],
    text = accuracyData["labels"],
    mode='markers',
    marker=dict(size=accuracyData['percent']/2.5,
                color=accuracyData['percent'])
))

fig.update_layout(
    title="TABLE III: Test accuracy using SVM classifier.",
    xaxis_title="Chunk of frequencies start",
    yaxis_title="Chunk of frequencies end",
    legend_title="Legend Title",
    font=dict(
        family="Courier New, monospace",
        size=18,
        color="RebeccaPurple"
    )
)


plotly.offline.plot(fig, filename = 'figure_1.html')
display(HTML('figure_1.html'))

#Test accuracy using k-means classifier.
import plotly.graph_objects as go
import plotly
from IPython.core.display import display, HTML
import pandas as pd
accuracyData = pd.DataFrame({'x': [0, 0, 0, 0, 0, 0, 0,100,100, 100,100,100,100, 200, 200, 200, 200, 200, 300, 300, 300, 300, 400,400,400, 500, 500,600],
                                    'labels': ['58%', '70%', '86%', '90%', '98%','100%','100%','72%','88%','90%','98%','100%','100%','86%','89%','99%','100%','100%','85%','98%','100%','100%','92%','100%','100%', '100%','100%','99%'],
                                    'y': [100, 200, 300, 400, 500, 600, 722,200,300, 400,500,600,722, 300,400,500,600,722,400,500,600,722,500,600,722,600,722, 722],
                                   'percent':[37, 37, 55, 56, 62,72,82,39,48,57,63,72,82,53,61,67,73,82,70,72,76,85,75,80,89, 83,91,94]})
fig = go.Figure(data=go.Scatter(
    x=accuracyData["x"],
    y=accuracyData["y"],
    text = accuracyData["labels"],
    mode='markers',
    marker=dict(size=accuracyData['percent']/2.5,
                color=accuracyData['percent'])
))

fig.update_layout(
    title="TABLE V: Test accuracy using k-means classifier.",
    xaxis_title="Chunk of frequencies start",
    yaxis_title="Chunk of frequencies end",
    legend_title="Legend Title",
    font=dict(
        family="Courier New, monospace",
        size=18,
        color="RebeccaPurple"
    )
)

plotly.offline.plot(fig, filename = 'figure_2.html')
display(HTML('figure_2.html'))

#Test accuracy using logistic regression classifier..
import plotly.graph_objects as go
import plotly
from IPython.core.display import display, HTML
import pandas as pd
accuracyData = pd.DataFrame({'x': [0, 0, 0, 0, 0, 0, 0,100,100, 100,100,100,100, 200, 200, 200, 200, 200, 300, 300, 300, 300, 400,400,400, 500, 500,600],
                                    'labels': ['58%', '70%', '86%', '90%', '98%','100%','100%','72%','88%','90%','98%','100%','100%','86%','89%','99%','100%','100%','85%','98%','100%','100%','92%','100%','100%', '100%','100%','99%'],
                                    'y': [100, 200, 300, 400, 500, 600, 722,200,300, 400,500,600,722, 300,400,500,600,722,400,500,600,722,500,600,722,600,722, 722],
                                   'percent':[58, 70, 86, 90, 98,100,100,72,88,90,98,100,100,86,89,99,100,100,85,98,100,100,92,100,100, 100,100,99]})
fig = go.Figure(data=go.Scatter(
    x=accuracyData["x"],
    y=accuracyData["y"],
    text = accuracyData["labels"],
    mode='markers',
    marker=dict(size=accuracyData['percent']/2.5,
                color=accuracyData['percent'])
))

fig.update_layout(
    title="TABLE IV: Test accuracy using logistic regression classifier.",
    xaxis_title="Chunk of frequencies start",
    yaxis_title="Chunk of frequencies end",
    legend_title="Legend Title",
    font=dict(
        family="Courier New, monospace",
        size=18,
        color="RebeccaPurple"
    )
)

plotly.offline.plot(fig, filename = 'figure_3.html')
display(HTML('figure_3.html'))